scholarly journals Population coding and oscillatory subspace synchronization integrate context into actions

2021 ◽  
Author(s):  
Jan Weber ◽  
Anne-Kristin Solbakk ◽  
Alejandro Blenkmann ◽  
Anais Llorens ◽  
Ingrid Funderud ◽  
...  
Keyword(s):  
Large Scale ◽  
Population Level ◽  
Population Coding ◽  
Action Plans ◽  
Contextual Cues ◽  
Population Activity ◽  
Scale Population ◽  
Hand Off ◽  
Neurophysiological Mechanisms ◽  
Context Dependent

Contextual cues and prior evidence guide human goal-directed behavior. To date, the neurophysiological mechanisms that implement contextual priors to guide subsequent actions remain unclear. Here we demonstrate that increasing behavioral uncertainty introduces a shift from an oscillatory to a continuous processing mode in human prefrontal cortex. At the population level, we found that oscillatory and continuous dynamics reflect dissociable signatures that support distinct aspects of encoding, transmission and execution of context-dependent action plans. We show that prefrontal population activity encodes predictive context and action plans in serially unfolding orthogonal subspaces, while prefrontal-motor theta oscillations synchronize action-encoding population subspaces to mediate the hand-off of action plans. Collectively, our results reveal how two key features of large-scale population activity, namely continuous population trajectories and oscillatory synchrony, operate in concert to guide context-dependent human behavior.

scholarly journals A Geometric Characterization of Population Coding in the Prefrontal Cortex and Hippocampus during a Paired-Associate Learning Task

2020 ◽  
Vol 32 (8) ◽  
pp. 1455-1465
Author(s):  
Yue Liu ◽  
Scott L. Brincat ◽  
Earl K. Miller ◽  
Michael E. Hasselmo
Keyword(s):  
Prefrontal Cortex ◽  
Large Scale ◽  
Paired Associate ◽  
Population Level ◽  
Population Coding ◽  
Task Structure ◽  
Population Activity ◽  
Associate Learning ◽  
Task Variables ◽  
Monkey Prefrontal Cortex

Large-scale neuronal recording techniques have enabled discoveries of population-level mechanisms for neural computation. However, it is not clear how these mechanisms form by trial-and-error learning. In this article, we present an initial effort to characterize the population activity in monkey prefrontal cortex (PFC) and hippocampus (HPC) during the learning phase of a paired-associate task. To analyze the population data, we introduce the normalized distance, a dimensionless metric that describes the encoding of cognitive variables from the geometrical relationship among neural trajectories in state space. It is found that PFC exhibits a more sustained encoding of the visual stimuli, whereas HPC only transiently encodes the identity of the associate stimuli. Surprisingly, after learning, the neural activity is not reorganized to reflect the task structure, raising the possibility that learning is accompanied by some “silent” mechanism that does not explicitly change the neural representations. We did find partial evidence on the learning-dependent changes for some of the task variables. This study shows the feasibility of using normalized distance as a metric to characterize and compare population-level encoding of task variables and suggests further directions to explore learning-dependent changes in the neural circuits.

scholarly journals A geometric characterization of population coding in the prefrontal cortex and hippocampus during a paired-associate learning task

2019 ◽  
Author(s):  
Yue Liu ◽  
Scott L Brincat ◽  
Earl K Miller ◽  
Michael E Hasselmo
Keyword(s):  
Prefrontal Cortex ◽  
Large Scale ◽  
Paired Associate ◽  
Population Data ◽  
Population Level ◽  
Population Coding ◽  
Population Activity ◽  
Associate Learning ◽  
Task Variables ◽  
Monkey Prefrontal Cortex

Large-scale neuronal recording techniques have enabled discoveries of population-level mechanisms for neural computation. However it is not clear how these mechanisms form by trial and error learning. In this paper we present an initial effort to characterize the population activity in monkey prefrontal cortex (PFC) and hippocampus (HPC) during the learning phase of a paired-associate task. To analyze the population data, we introduce the normalized distance, a dimensionless metric that describes the encoding of cognitive variables from the geometrical relationship among neural trajectories in state space. It is found that PFC exhibits a more sustained encoding of task-relevant variables whereas HPC only transiently encodes the identity of the stimuli. We also found partial evidence on the learning-dependent changes for some of the task variables. This study shows the feasibility of using normalized distance as a metric to characterize and compare population level encoding of task variables, and suggests further directions to explore the learning-dependent changes in the population activity.

scholarly journals Orientation and color tuning of the human visual gamma rhythm

2021 ◽  
Author(s):  
Ye Li ◽  
William Bosking ◽  
Michael S Beauchamp ◽  
Sameer A Sheth ◽  
Daniel Yoshor ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Large Scale ◽  
Brain Activity ◽  
Population Level ◽  
Gamma Oscillations ◽  
Cell Orientation ◽  
Population Activity ◽  
Color Tuning ◽  
Human Visual Cortex ◽  
Gamma Rhythm

Narrowband gamma oscillations (NBG: ~20-60Hz) in visual cortex reflect rhythmic fluctuations in population activity generated by underlying circuits tuned for stimulus location, orientation, and color. Consequently, the amplitude and frequency of induced NBG activity is highly sensitive to these stimulus features. For example, in the non-human primate, NBG displays biases in orientation and color tuning at the population level. Such biases may relate to recent reports describing the large-scale organization of single-cell orientation and color tuning in visual cortex, thus providing a potential bridge between measurements made at different scales. Similar biases in NBG population tuning have been predicted to exist in the human visual cortex, but this has yet to be fully examined. Using intracranial recordings from human visual cortex, we investigated the tuning of NBG to orientation and color, both independently and in conjunction. NBG was shown to display a cardinal orientation bias (horizontal) and also an end- and mid-spectral color bias (red/blue and green). When jointly probed, the cardinal bias for orientation was attenuated and an end-spectral preference for red and blue predominated. These data both elaborate on the close, yet complex, link between the population dynamics driving NBG oscillations and known feature selectivity biases in visual cortex, adding to a growing set of stimulus dependencies associated with the genesis of NBG. Together, these two factors may provide a fruitful testing ground for examining multi-scale models of brain activity, and impose new constraints on the functional significance of the visual gamma rhythm.

scholarly journals Representational drift in the mouse visual cortex

2020 ◽  
Author(s):  
Daniel Deitch ◽  
Alon Rubin ◽  
Yaniv Ziv
Keyword(s):  
Large Scale ◽  
Activity Patterns ◽  
Population Level ◽  
Population Activity ◽  
Cortical Areas ◽  
Electrophysiological Recordings ◽  
The Face ◽  
Visual Cortical ◽  
Mouse Visual Cortex ◽  
Over Time

AbstractNeuronal representations in the hippocampus and related structures gradually change over time despite no changes in the environment or behavior. The extent to which such ‘representational drift’ occurs in sensory cortical areas and whether the hierarchy of information flow across areas affects neural-code stability have remained elusive. Here, we address these questions by analyzing large-scale optical and electrophysiological recordings from six visual cortical areas in behaving mice that were repeatedly presented with the same natural movies. We found representational drift over timescales spanning minutes to days across multiple visual areas. The drift was driven mostly by changes in individual cells’ activity rates, while their tuning changed to a lesser extent. Despite these changes, the structure of relationships between the population activity patterns remained stable and stereotypic, allowing robust maintenance of information over time. Such population-level organization may underlie stable visual perception in the face of continuous changes in neuronal responses.

scholarly journals The human functional genome defined by genetic diversity

2016 ◽  
Author(s):  
Julia di Iulio ◽  
Istvan Bartha ◽  
Emily H.M. Wong ◽  
Hung-Chun Yu ◽  
Michael Hicks ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Large Scale ◽  
Genomic Organization ◽  
Population Level ◽  
Population Diversity ◽  
Sequence Conservation ◽  
Pathogenic Variants ◽  
Human Genomes ◽  
Wide Variability ◽  
Context Dependent

ABSTRACTLarge scale efforts to sequence whole human genomes provide extensive data on the non-coding portion of the genome. We used variation information from 11,257 human genomes to describe the spectrum of sequence conservation in the population. We established the genome-wide variability for each nucleotide in the context of the surrounding sequence in order to identify departure from expectation at the population level (context-dependent conservation). We characterized the population diversity for functional elements in the genome and identified the coordination of conserved sequences of distal and cis enhancers, chromatin marks, promoters, coding and intronic regions. The most context-dependent conserved regions of the genome are associated with unique functional annotations and a genomic organization that spreads up to one megabase. Importantly, these regions are enriched by over 100-fold of non-coding pathogenic variants. This analysis of human genetic diversity thus provides a detailed view of sequence conservation, functional constraint and genomic organization of the human genome. Specifically, it identifies highly conserved non-coding sequences that are not captured by analysis of interspecies conservation and are greatly enriched in disease variants.

scholarly journals Modelling the neural code in large populations of correlated neurons

2020 ◽  
Author(s):  
Sacha Sokoloski ◽  
Amir Aschner ◽  
Ruben Coen-Cagli
Keyword(s):  
Neural Coding ◽  
Large Scale ◽  
Neural Code ◽  
Neural Population ◽  
Population Activity ◽  
Fundamental Properties ◽  
Wide Range ◽  
Large Populations ◽  
Scale Population ◽  
Quantitative Understanding

AbstractThe activity of a neural population encodes information about the stimulus that caused it, and decoding population activity reveals how neural circuits process that information. Correlations between neurons strongly impact both encoding and decoding, yet we still lack models that simultaneously capture stimulus encoding by large populations of correlated neurons and allow for accurate decoding of stimulus information, thus limiting our quantitative understanding of the neural code. To address this, we propose a class of models of large-scale population activity based on the theory of exponential family distributions. We apply our models to macaque primary visual cortex (V1) recordings, and show they capture a wide range of response statistics, facilitate accurate Bayesian decoding, and provide interpretable representations of fundamental properties of the neural code. Ultimately, our framework could allow researchers to quantitatively validate predictions of theories of neural coding against both large-scale response recordings and cognitive performance.

scholarly journals Reliability of the NIH toolbox cognitive battery in children and adolescents: a 3-year longitudinal examination

2020 ◽  
pp. 1-10
Author(s):  
Brittany K. Taylor ◽  
Michaela R. Frenzel ◽  
Jacob A. Eastman ◽  
Alex I. Wiesman ◽  
Yu-Ping Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Intraclass Correlation ◽  
Temporal Stability ◽  
Correlation Coefficients ◽  
Population Level ◽  
Clinical Applications ◽  
Intraclass Correlation Coefficients ◽  
Scale Population ◽  
Longitudinal Examination

Abstract Background The Cognitive Battery of the National Institutes of Health Toolbox (NIH-TB) is a collection of assessments that have been adapted and normed for administration across the lifespan and is increasingly used in large-scale population-level research. However, despite increasing adoption in longitudinal investigations of neurocognitive development, and growing recommendations that the Toolbox be used in clinical applications, little is known about the long-term temporal stability of the NIH-TB, particularly in youth. Methods The present study examined the long-term temporal reliability of the NIH-TB in a large cohort of youth (9–15 years-old) recruited across two data collection sites. Participants were invited to complete testing annually for 3 years. Results Reliability was generally low-to-moderate, with intraclass correlation coefficients ranging between 0.31 and 0.76 for the full sample. There were multiple significant differences between sites, with one site generally exhibiting stronger temporal stability than the other. Conclusions Reliability of the NIH-TB Cognitive Battery was lower than expected given early work examining shorter test-retest intervals. Moreover, there were very few instances of tests meeting stability requirements for use in research; none of the tests exhibited adequate reliability for use in clinical applications. Reliability is paramount to establishing the validity of the tool, thus the constructs assessed by the NIH-TB may vary over time in youth. We recommend further refinement of the NIH-TB Cognitive Battery and its norming procedures for children before further adoption as a neuropsychological assessment. We also urge researchers who have already employed the NIH-TB in their studies to interpret their results with caution.

scholarly journals The Population Tracking Model: A Simple, Scalable Statistical Model for Neural Population Data

2017 ◽  
Vol 29 (1) ◽  
pp. 50-93 ◽  
Author(s):  
Cian O’Donnell ◽  
J. Tiago Gonçalves ◽  
Nick Whiteley ◽  
Carlos Portera-Cailliau ◽  
Terrence J. Sejnowski
Keyword(s):  
Statistical Model ◽  
Large Scale ◽  
Population Data ◽  
Population Coding ◽  
Network Activity ◽  
Neural Population ◽  
Population Activity ◽  
Tracking Model ◽  
Population Rate ◽  
Neural Population Activity

Our understanding of neural population coding has been limited by a lack of analysis methods to characterize spiking data from large populations. The biggest challenge comes from the fact that the number of possible network activity patterns scales exponentially with the number of neurons recorded ([Formula: see text]). Here we introduce a new statistical method for characterizing neural population activity that requires semi-independent fitting of only as many parameters as the square of the number of neurons, requiring drastically smaller data sets and minimal computation time. The model works by matching the population rate (the number of neurons synchronously active) and the probability that each individual neuron fires given the population rate. We found that this model can accurately fit synthetic data from up to 1000 neurons. We also found that the model could rapidly decode visual stimuli from neural population data from macaque primary visual cortex about 65 ms after stimulus onset. Finally, we used the model to estimate the entropy of neural population activity in developing mouse somatosensory cortex and, surprisingly, found that it first increases, and then decreases during development. This statistical model opens new options for interrogating neural population data and can bolster the use of modern large-scale in vivo Ca[Formula: see text] and voltage imaging tools.

Learnings from Large-Scale, Population-Level Implementation of Parenting Support

2017 ◽  
pp. 419-421
Author(s):  
Trevor G. Mazzucchelli
Keyword(s):  
Large Scale ◽  
Best Practice ◽  
Systems Approach ◽  
Positive Impact ◽  
Population Level ◽  
The United States ◽  
Parenting Support ◽  
Scale Population ◽  
The Many ◽  
Learning Principles

There is considerable evidence supporting the efficacy and effectiveness of parenting interventions based on social learning principles for a range of social, emotional, and health problems, involving different types of families and through a variety of delivery systems. The challenge now is “going to scale” in order to have a positive impact at a population level. This chapter introduces three best practice exemplars that have taken place in the United States, Ireland, and Australia, where a full multilevel systems approach to parenting support has been applied and evaluated. These applications provide important lessons regarding the barriers and facilitators that can influence an initiative’s success and degree of impact. By illustrating how these approaches have involved different populations, behavioral targets, evaluation designs, and means of assessing outcome, they also hint at the many possibilities that are available in future dissemination efforts.

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